论文标题
第三名解决方案“ Google Landmark检索2020”
3rd Place Solution to "Google Landmark Retrieval 2020"
论文作者
论文摘要
图像检索是计算机视觉中的基本问题。本文介绍了我们针对2020年Google Landmark检索挑战的第三名详细解决方案。我们专注于通过度量学习对数据清洁和模型的探索。我们使用基于嵌入聚类的数据清洁策略。此外，我们采用了一种称为Corner-Cutmix的数据增强方法，该方法提高了该模型识别多尺度和遮挡的地标图像的能力。我们详细介绍了方法的消融实验和结果。
Image retrieval is a fundamental problem in computer vision. This paper presents our 3rd place detailed solution to the Google Landmark Retrieval 2020 challenge. We focus on the exploration of data cleaning and models with metric learning. We use a data cleaning strategy based on embedding clustering. Besides, we employ a data augmentation method called Corner-Cutmix, which improves the model's ability to recognize multi-scale and occluded landmark images. We show in detail the ablation experiments and results of our method.
